A general recurrent state space framework for modeling neural dynamics during decision-making

David Zoltowski, Jonathan Pillow, Scott Linderman
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:11680-11691, 2020.

Abstract

An open question in systems and computational neuroscience is how neural circuits accumulate evidence towards a decision. Fitting models of decision-making theory to neural activity helps answer this question, but current approaches limit the number of these models that we can fit to neural data. Here we propose a general framework for modeling neural activity during decision-making. The framework includes the canonical drift-diffusion model and enables extensions such as multi-dimensional accumulators, variable and collapsing boundaries, and discrete jumps. Our framework is based on constraining the parameters of recurrent state space models, for which we introduce a scalable variational Laplace EM inference algorithm. We applied the modeling approach to spiking responses recorded from monkey parietal cortex during two decision-making tasks. We found that a two-dimensional accumulator better captured the responses of a set of parietal neurons than a single accumulator model, and we identified a variable lower boundary in the responses of a parietal neuron during a random dot motion task. We expect this framework will be useful for modeling neural dynamics in a variety of decision-making settings.

Cite this Paper

BibTeX
@InProceedings{pmlr-v119-zoltowski20a, title = {A general recurrent state space framework for modeling neural dynamics during decision-making}, author = {Zoltowski, David and Pillow, Jonathan and Linderman, Scott}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {11680--11691}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/zoltowski20a/zoltowski20a.pdf}, url = {https://proceedings.mlr.press/v119/zoltowski20a.html}, abstract = {An open question in systems and computational neuroscience is how neural circuits accumulate evidence towards a decision. Fitting models of decision-making theory to neural activity helps answer this question, but current approaches limit the number of these models that we can fit to neural data. Here we propose a general framework for modeling neural activity during decision-making. The framework includes the canonical drift-diffusion model and enables extensions such as multi-dimensional accumulators, variable and collapsing boundaries, and discrete jumps. Our framework is based on constraining the parameters of recurrent state space models, for which we introduce a scalable variational Laplace EM inference algorithm. We applied the modeling approach to spiking responses recorded from monkey parietal cortex during two decision-making tasks. We found that a two-dimensional accumulator better captured the responses of a set of parietal neurons than a single accumulator model, and we identified a variable lower boundary in the responses of a parietal neuron during a random dot motion task. We expect this framework will be useful for modeling neural dynamics in a variety of decision-making settings.} }
Endnote
%0 Conference Paper %T A general recurrent state space framework for modeling neural dynamics during decision-making %A David Zoltowski %A Jonathan Pillow %A Scott Linderman %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-zoltowski20a %I PMLR %P 11680--11691 %U https://proceedings.mlr.press/v119/zoltowski20a.html %V 119 %X An open question in systems and computational neuroscience is how neural circuits accumulate evidence towards a decision. Fitting models of decision-making theory to neural activity helps answer this question, but current approaches limit the number of these models that we can fit to neural data. Here we propose a general framework for modeling neural activity during decision-making. The framework includes the canonical drift-diffusion model and enables extensions such as multi-dimensional accumulators, variable and collapsing boundaries, and discrete jumps. Our framework is based on constraining the parameters of recurrent state space models, for which we introduce a scalable variational Laplace EM inference algorithm. We applied the modeling approach to spiking responses recorded from monkey parietal cortex during two decision-making tasks. We found that a two-dimensional accumulator better captured the responses of a set of parietal neurons than a single accumulator model, and we identified a variable lower boundary in the responses of a parietal neuron during a random dot motion task. We expect this framework will be useful for modeling neural dynamics in a variety of decision-making settings.
APA
Zoltowski, D., Pillow, J. & Linderman, S.. (2020). A general recurrent state space framework for modeling neural dynamics during decision-making. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:11680-11691 Available from https://proceedings.mlr.press/v119/zoltowski20a.html.

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